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As we move into 2018, Analytical Data Infrastructure (ADI) is becoming a significant topic in business intelligence and analytics. Where Big Data was once an over-hyped, catch-all term, in the coming year we will see organisations move to a place where business-oriented ‘data strategies’ are the major focus. With that shift comes the need for sophisticated, yet easy to use, data science approaches that deliver results back to the business.

It is a point backed up by the 2018 Global Dresner Market Study for Analytical Data Infrastructure (ADI). The highly-regarded report revealed the key priorities for businesses for their data analytics and business intelligence efforts. From deployment to loading priorities, data preparation, modelling and management of data associated with ADI, the study captured the most important and current market trends driving the intelligent adoption of Data Science.

The Dresner report explored the ways in which end-users are planning to invest in ADI technology in the year ahead, along with the considerations behind implementation and use-cases. While security and performance were listed as the top two priorities for businesses, an interesting finding was that the biggest year-on-year change was the growing importance of easy access to and use of analytical features and programming languages such as the use of R, Machine Learning technology and MapReduce analytics.

Businesses have woken up to the fact that there is value in their data. With the right tools, they can extract that value – tapping into insight to improve the way they sell to their customers, or to streamline business processes and reduce costs.

But often, data has to be extracted, cleansed and transferred to other systems. In most companies, the Business Intelligence competence centres are separate teams to the Data Science teams, and they rarely work closely together. Modern analytics platforms combine these two worlds and allow to do SQL-based data analytics, Map Reduce algorithms and data science languages such as R or Python side by side. Many database vendors offer such capabilities, and some have even integrated these languages tightly into databases, allowing organisations to run data science on huge data sets.

While cleansing the data and finding the right models is a repetitive task that is sufficient to run on smaller data sets, high performance in-memory computing can make a vast difference when applying created R or Python models to billions of user data, in near-real-time.

Letting analysts use the data science tools of choice

Data analysts have their favoured analytics and visualisation tools which either leads to a wide spread of different tools that have to be integrated and maintained in the data management eco system, or to people not cooperating with each other. Further, the actual data science scripting language is often a personal preference. Each language has its own strengths and weaknesses in relation to the complexity of the task or features that the language offers.

As we move from an era of descriptive (looking at past trends), to predictive (looking to the future) and even to prescriptive (finding the best course of action to meet key performance indicators) for the most advanced analysis, the combination of AI and standard SQL analytics can create more agility and efficiency in finding the right insights out of data.

The good news is that there are platforms that combine any data science language within the same system, and combine it with standard database technologies. Exasol version 6.0 has for instance an open-sourced integration framework that allows to install any programming language and use it directly inside the SQL database. Pre-shipped languages are R, Python, Java and Lua, but you can also create containers for Julia, Scala, C++ or your choice.

Did you ever think it would be possible to provide normal SQL analysts access to data science results? Or that it would be possible to conduct powerful data processing in SQL rather than the programming languages? This leads to more flexibility, but essentially to exceptional performance.

Technologies has to follow your strategy

It will be interesting to see how data science technology evolves over time, and how companies move to leverage all possible ways of creating insights, predictions and automated prescriptions out of all kinds of data. This is not just a question of people’s skill sets or certain algorithms, but also the right architecture for your data eco system. It should facilitate data storage, standard reporting and data processing, artificial intelligence and a flexible way of adjusting to future trends in an open, extensible platform.

The technology should be available in the necessary ways – from a free downloadable solution to let developers play around on their laptops, to high-performance on-premise systems in your secured data center up to the standard public cloud platforms such as Amazon, Azure or Google. The technology should follow your data strategy, not the other way round. https://goo.gl/jRyHcB #DataScience #Cloud

Working in Data Science recruitment, we’re no strangers to the mountains you have to climb and pitfalls faced when getting into a Data Science career. Despite the mounting demand for Data Science professionals, it’s still an extremely difficult career path to break into. The most common complaints we see from candidates who have faced rejection are lack of experience, education level requirements, lack of opportunities for Freshers, overly demanding and confusing job role requirements.

Experience

First of all, let’s tackle what seems to be what seems the hardest obstacle to overcome, lack of experience. This is a complex one and not just applicable to Data Science, across professions it’s a common complaint that entry-level jobs ask for years’ worth of experience. Every company wants an experienced data scientist, but with the extremely fast emergence of the field and growing demand for professionals, there is not enough to go around! Our advice here for anyone trying to get into Data Science who is lacking experience is to try and get an internship by contacting companies directly. Sometimes, you will find these types of positions available with recruiters but you will no doubt have more luck going direct.

Another approach is to have a go at Kaggle competitions, write code and put this on GitHub for people to see. There are many ways you can gain experience in your spare time without this being in a business setting, in a way that a hiring manager will notice. If you have the time free too, think of offering free consultations to friends or businesses and build on opportunities like that. Go beyond publishing code on GitHub, and write a detailed post of your analysis and code on a blog, data site or even LinkedIn. This gives you even more exposure and exemplifies your deep understanding of what you do. There are also challenges for people with heaps of experience getting rejected due to ‘lack of experience’ and the truth is, is that lack of experience often translates to you have a lack of applicable experience to the role you’re applying for. To overcome these obstacles, make sure you’re reading job descriptions properly, researching the company and tailoring your resume to highlight how you are what they’re looking for.

Deciphering Job Descriptions

The growing demand for Data Scientists in a number of different industries, specializing in different fields means that it can be difficult for employers to define a reasonable, ‘blanket’ skill set required, which can lead to a lot of confusion for those starting out. Beyond knowing that a good Data Scientist needs to be a critical thinker, analytically minded, a great communicator and have a passion for the field, technical requirements and experience needed can vary greatly between roles and companies. Try not to be overwhelmed when looking at job descriptions. It’s important to remember that many companies will put on more skills and experience than actually needed into the job descriptions. So, even if you hold half of the skills they’re asking for, but make up for the rest in willingness to learn/passion for the role/transferable skills, then go for it – don’t be put off. If you’re not confident in doing so, try seeing the patterns in what is being asked for, highlighting the top required skills for the roles you want to apply for and take some time in getting better at these.

Reaching out

Many professionals, whilst having the qualifications needed, lack basic skills needed when it comes to communicating with hiring managers and recruiters. Commenting on LinkedIn posts asking for a review of your profile is not going to cut it, I’m afraid. Reach out directly to those that are posting the job adverts or if it’s a company, do some research and find the hiring manager or recruitment team. They’ll appreciate the direct approach, and you’ll be able to provide more information on why you should be considered for the role. It might seem like a good way to get noticed as CV’s can get lost in the mountains that recruiters receive… but this is where resume skills come in to play and knowing how to get yours noticed.

Resume skills

You’ve more than likely got some great points on your CV, experiences, and projects that are noteworthy but often, your CV will also be littered with irrelevant information to pad it out – especially if you’re just starting out. Our advice? Get rid of the filler, get to the point and highlight how you can make a difference where you’re applying to.

Make sure your skills, experience, and projects tell the hiring manager that you have the tools necessary to make an impact on their business and how when applying these techniques in the past, you’ve had x y z results. Quantify these results – how did it benefit the company in terms of revenue, ROI, time-saving or costs? Tailor your CV, don’t just send generic ones out. Exhibit your understanding of the fundamentals, that you have proficient knowledge of the foundations of data science and the rest will follow.

The layout is also important, hire a designer or put in some hours on free platforms out there that can help with this. Even on Word, you can create an interesting, eye-catching layout! You can see more on mastering your resume here. Another great way to soak up as much information about

Data Science is to follow influencers in your field on social media, especially LinkedIn – there are often really insightful posts, you can reach out to the data science community, learn new things, post questions and see current opportunities available.

Have you any other top tips for getting into Data Science? Please share in the comments! https://goo.gl/8W2tTJ #DataScience #Cloud